Pre-deployment policy
These pre-deployment policies are for those who are just starting out. Some of these strategies will be more suitable for just getting startedcloud computingdeployed teams; others are suitable for existing environments that have not been deployed yetKubernetesuser.
1. Choose a single (rather than multiple) cloud computing provider
Although multi-cloud architectures usually provide a largeflexibility, but when it comes to Kubernetes, they usually incur higher costs. This has to do with the different ways of providing Kubernetes.
existAWSOn the above, EKS is the main way for users to access Kubernetes; on Azure, the main way for accessing Kubernetes has become AKS. Each of them is built on the core Kubernetes architecture, but is used in a completely different way.
Cloud computing providers have their own implementations, extensions, best practices and unique features, which means that the functionality that can be cost-optimized well on EKS doesn't work on AKS (or isn't an option at all). Not to mention managing the operating costs of Kubernetes through multiple services and the need to understand the cost optimization problems that a multi-cloud environment has.
Therefore, it is recommended that a single provider is better to choose from, given the issues such as cost and complexity.
2. Choose the correct architecture
For those who are in the early stages of their journey to cloud and Kubernetes, the cost of cloud computing (and everything else) will be significantly affected by the type of architecture you choose. When it comes to Kubernetes, here are some things to pay attention to.
You probably know that if you use Kubernetes clusters or containers more generally, based onMicroservicesThe architecture will be very suitable. Monolithic Applications will not be able to take full advantage of containerization.
However, there are other unknown considerations. For example, stateful applications (Stateful Applications, such as SQL databases) are not suitable for containers. Likewise, applications that require custom hardware (such as those used in large quantities).AI/ML) is also not ideal for Kubernetes.
So, after choosing a cloud provider, it’s better to consider the degree to which Kubernetes and containers are adopted and then make an informed choice for your architecture.
Post-deployment policy
These strategies are suitable for organizations that are already using Kubernetes and are looking for new ways to achieve the highest cost efficiency.
3. Set the correct resource limits and quotas, as well as appropriate expansion methods
Resource limits and quotas can limit how you spend, and without these, any Kubernetes cluster will run in an unpredictable way. If no limit is set for any pod in the cluster, a pod can easily run out of memory and CPU.
For example, if you have a front-end pod, the peak of user traffic will mean the peak of consumption. While you don't want your application to crash, consuming resources without limit is not the solution.
Instead, you need reasonable resource constraints and other strategies to handle heavy usage. At this point, optimizing the performance of your application will be a better way to ensure that customer needs are met without additional costs.
The same is true for quotas, although they are applied to namespace levels and other types of resources. But essentially, it is based on careful setting of restrictions and other appropriate methods to ensure your delivery.
4. Set intelligent automatic expansion rules
When it comes to automatic scaling in Kubernetes, you have two options: horizontal scaling and vertical scaling. You will use a rule-based system to decide what to do under what conditions.
Horizontal scaling means increasing the total number of pods, while vertical scaling means increasing the memory and CPU capacity of the pod without increasing the total number. Each approach has its advantages when it comes to ideal resource use and avoiding unnecessary costs.
Horizontal scaling is a better choice when you need to scale quickly. Additionally, because the more pods you have, the less likely a single point of failure will cause a crash, horizontal scaling is desirable when allocating a large amount of traffic. It is also a better choice when running stateless applications, as additional pods are able to better handle multiple concurrent requests.
Vertical scaling is more beneficial for stateful applications, because it is easier to keep state by adding resources to the pod than propagating the state in a new pod. Vertical scaling is also desirable when you have other limitations on scaling, such as limited IP address space or the limit on the number of nodes imposed by a license.
When defining extension rules, you need to understand the use cases of each rule, the characteristics of the application, and the types of extension requirements that may be met.
5. Use Rightsizing
Rightsizing is not complicated to say, it just adjusts the specifications of the resource to the specifications it actually needs to use. In the Kubernetes context, it means ensuring the appropriate resource utilization (CPU and memory) for each pod and node in the Kubernetes environment. If you don't resize correctly, something can happen that affects application performance and cost optimization efforts.
In case of over-configuration, paid CPU and memory may be unused and become idle resources; in case of under-supply, although it will not directly affect the cost of Kubernetes, it will cause performance problems and ultimately lead to cost reductions.
There are some ways to be available when it comes to Rightsizing. It can be done manually by engineers or fully automated using tools. In short, Rightsizing is an ongoing process that requires dynamic adjustment, but if done well, it is an important part of a cost optimization strategy.
6. Make full use of Spot instances
Spot instances are ideal for certain situations. If your application can handle unpredictability, you can get huge discounts on instances for a limited time (up to 90% on AWS). However, there may be some additional configuration.
For example, you will need to adjust the pod distribution budget and set up Readiness Probes to prepare a Kubernetes cluster for sudden deletion of instances.
The same is true for node management - you need to diversify instance types and pods to deal with interrupts.
Spot instances are a great way to reduce application costs, but integrating this unpredictability into Kubernetes requires expertise.
7. Strategically utilize regional resources to reduce traffic
An often overlooked cost optimization strategy is to reduce traffic between different geographical regions. When nodes cover multiple areas, data transfer fees may increase rapidly as you will use the public internet to send and receive data. Here, tools like AWS Private Link and Azure Private Link can help you optimize costs by providing alternative routing.
Regional distribution and data transfer strategies of a cluster can be a complex task, and some people will use tools to do the operation, but once it is done, it is a great way to reduce monthly bills.
Continuous improvement strategies
These Kubernetes cost optimization techniques are suitable for organizations that may have solved the most common problems and want to achieve continuous improvement. If you are well aware of practices such as automatic scaling and Rightsizing, here are some practical cost management techniques for more experienced Kubernetes users.
8. Use monitoring costs to improve efficiency
Kubernetes, EKS, AKS and GKE all offer their own cost monitoring and optimization capabilities. But to get really meticulous insights, it is usually best to invest in third-party tools. There are many Kubernetes cost optimization tools to choose from. There are some common cloud cost management tools available on the market that work well with Kubernetes infrastructure.
Generally speaking, when you choose a tool, you should prioritize what you lack the most. Some tools are best for generating insight; some focus onAI, which means less control and user input, which is a good thing for teams lacking HR.
In short, consider what is missing in the Kubernetes cost optimization process and choose the right tools according to your needs.
9. Integrate cost control intoCI/CDIn the pipeline
If your organization willDevOpsIn conjunction with Kubernetes, you can build Kubernetes cost monitoring and control into a CI/CD pipeline at different points.
For example, when integrated correctly, Kubecost can be used to predict the cost of changes before deployment. It can also be used to automate cost-related controls that can even lead to build failure if the forecasted cost is too high. More broadly, integrating Kubecost (or scripts with similar functionality) can allow Kubernetes to overwrite a monitorable data point to provide data for future CI/CD decisions.
So if you are using Kubernetes and your organization has adopted DevOps, you can build cost optimizations to the core of your process.
10. Create an environment that enables cost optimization through tools and culture
Although this involves the overall cost of cloud computing, it is worth taking the time to list some key points.
First, if you have done some post-deployment and ongoing work, it will be easier to adopt the right mindset throughout your organization, which requires data. Therefore, having the right cost monitoring and optimization tools is a good start, and Kubefed, CAST AI or Densify are all good choices.
Second, these data need to be accessible and meaningful to multiple stakeholders. If you already adopt DevOps, this shouldn't be that difficult. But if you don't have one, you may encounter a little resistance. Tools like Apptio Cloudability can help achieve this, providing clear cost insights and focusing on connecting non-technical stakeholders to key statistics.